Identification and genetic analysis of cancer cells with PCR-activated cell sorting.
Identification and genetic analysis of cancer cells with PCR-activated cell sorting.
复制标题
通过 PCR 激活细胞分选对癌细胞进行鉴定和遗传分析。
DOI：
10.1093/nar/gku606
复制
发表时间：
2014
影响因子：
14.9
通讯作者：
Abate AR
中科院分区：
文献类型：
--
作者：
Eastburn DJ;Sciambi A;Abate AR
细胞分选是生命科学研究中分析细胞异质性或从大种群中丰富稀有细胞的核心工具。虽然像FACS和FISH-FC这样的方法可以从不同的群体中鉴定和分离细胞，但它们受到对抗体的依赖，或者需要化学固定细胞。我们介绍了一种新的细胞分类技术，它基于细胞核酸的序列特异性分析进行稳健的分类。我们的方法，聚合酶链式反应激活的细胞分选(PACS)，使用TaqMan聚合酶链式反应来检测单个细胞内的核酸并触发它们的分选。用这种方法，我们通过同时进行132 000个针对波形蛋白mRNA的单细胞TaqMan RT-PCR反应，从一个不同的群体中鉴定和分选了前列腺癌细胞。在波形蛋白阳性的液滴分类和回收的核酸的下游分析之后，我们发现癌症特异性基因组和转录本显著丰富。此外，我们还证明了PACS可用于通过针对单拷贝基因组DNA的TaqMan聚合酶链式反应来对细胞进行分选和浓缩。PACS提供了一种新的通用技术能力，通过允许基于现有方法无法接受的细胞信息进行分选，从而扩展了细胞分选的应用空间。
Cell sorting is a central tool in life science research for analyzing cellular heterogeneity or enriching rare cells out of large populations. Although methods like FACS and FISH-FC can characterize and isolate cells from heterogeneous populations, they are limited by their reliance on antibodies, or the requirement to chemically fix cells. We introduce a new cell sorting technology that robustly sorts based on sequence-specific analysis of cellular nucleic acids. Our approach, PCR-activated cell sorting (PACS), uses TaqMan PCR to detect nucleic acids within single cells and trigger their sorting. With this method, we identified and sorted prostate cancer cells from a heterogeneous population by performing >132 000 simultaneous single-cell TaqMan RT-PCR reactions targeting vimentin mRNA. Following vimentin-positive droplet sorting and downstream analysis of recovered nucleic acids, we found that cancer-specific genomes and transcripts were significantly enriched. Additionally, we demonstrate that PACS can be used to sort and enrich cells via TaqMan PCR reactions targeting single-copy genomic DNA. PACS provides a general new technical capability that expands the application space of cell sorting by enabling sorting based on cellular information not amenable to existing approaches.
登录
查看更多内容
影响因子：
14.8
作者：
Mazutis, Linas;Gilbert, John;Ung, W. Lloyd;Weitz, David A.;Griffiths, Andrew D.;Heyman, John A.
通讯作者：
Heyman, John A.
DOI：
10.1073/pnas.0704958104
发表时间：
2007-11-27
影响因子：
11.1
作者：
Di Carlo, Dino;Irimia, Daniel;Toner, Mehmet
通讯作者：
Toner, Mehmet
影响因子：
3.7
作者：
Eastburn DJ;Sciambi A;Abate AR
通讯作者：
Abate AR
影响因子：
48
作者：
Kalisky, Tomer;Quake, Stephen R.
通讯作者：
Quake, Stephen R.
影响因子：
3.2
作者：
Mary, Pascaline;Dauphinot, Luce;Tabeling, Patrick
通讯作者：
Tabeling, Patrick